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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.ensemble</span></code>.StackingClassifier</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-ensemble-stackingclassifier">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.ensemble.StackingClassifier</span></code></a></li>
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  <div class="section" id="sklearn-ensemble-stackingclassifier">
<h1><a class="reference internal" href="../classes.html#module-sklearn.ensemble" title="sklearn.ensemble"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.ensemble</span></code></a>.StackingClassifier<a class="headerlink" href="#sklearn-ensemble-stackingclassifier" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.ensemble.StackingClassifier">
<em class="property">class </em><code class="sig-prename descclassname">sklearn.ensemble.</code><code class="sig-name descname">StackingClassifier</code><span class="sig-paren">(</span><em class="sig-param">estimators</em>, <em class="sig-param">final_estimator=None</em>, <em class="sig-param">cv=None</em>, <em class="sig-param">stack_method='auto'</em>, <em class="sig-param">n_jobs=None</em>, <em class="sig-param">passthrough=False</em>, <em class="sig-param">verbose=0</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/ensemble/_stacking.py#L241"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier" title="Permalink to this definition">¶</a></dt>
<dd><p>Stack of estimators with a final classifier.</p>
<p>Stacked generalization consists in stacking the output of individual
estimator and use a classifier to compute the final prediction. Stacking
allows to use the strength of each individual estimator by using their
output as input of a final estimator.</p>
<p>Note that <code class="docutils literal notranslate"><span class="pre">estimators_</span></code> are fitted on the full <code class="docutils literal notranslate"><span class="pre">X</span></code> while <code class="docutils literal notranslate"><span class="pre">final_estimator_</span></code>
is trained using cross-validated predictions of the base estimators using
<code class="docutils literal notranslate"><span class="pre">cross_val_predict</span></code>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.22.</span></p>
</div>
<p>Read more in the <a class="reference internal" href="../ensemble.html#stacking"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>estimators</strong><span class="classifier">list of (str, estimator)</span></dt><dd><p>Base estimators which will be stacked together. Each element of the
list is defined as a tuple of string (i.e. name) and an estimator
instance. An estimator can be set to ‘drop’ using <code class="docutils literal notranslate"><span class="pre">set_params</span></code>.</p>
</dd>
<dt><strong>final_estimator</strong><span class="classifier">estimator, default=None</span></dt><dd><p>A classifier which will be used to combine the base estimators.
The default classifier is a <code class="docutils literal notranslate"><span class="pre">LogisticRegression</span></code>.</p>
</dd>
<dt><strong>cv</strong><span class="classifier">int, cross-validation generator or an iterable, default=None</span></dt><dd><p>Determines the cross-validation splitting strategy used in
<code class="docutils literal notranslate"><span class="pre">cross_val_predict</span></code> to train <code class="docutils literal notranslate"><span class="pre">final_estimator</span></code>. Possible inputs for
cv are:</p>
<ul class="simple">
<li><p>None, to use the default 5-fold cross validation,</p></li>
<li><p>integer, to specify the number of folds in a (Stratified) KFold,</p></li>
<li><p>An object to be used as a cross-validation generator,</p></li>
<li><p>An iterable yielding train, test splits.</p></li>
</ul>
<p>For integer/None inputs, if the estimator is a classifier and y is
either binary or multiclass, <code class="docutils literal notranslate"><span class="pre">StratifiedKFold</span></code> is used. In all other
cases, <code class="docutils literal notranslate"><span class="pre">KFold</span></code> is used.</p>
<p>Refer <a class="reference internal" href="../cross_validation.html#cross-validation"><span class="std std-ref">User Guide</span></a> for the various
cross-validation strategies that can be used here.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>A larger number of split will provide no benefits if the number
of training samples is large enough. Indeed, the training time
will increase. <code class="docutils literal notranslate"><span class="pre">cv</span></code> is not used for model evaluation but for
prediction.</p>
</div>
</dd>
<dt><strong>stack_method</strong><span class="classifier">{‘auto’, ‘predict_proba’, ‘decision_function’, ‘predict’},             default=’auto’</span></dt><dd><p>Methods called for each base estimator. It can be:</p>
<ul class="simple">
<li><p>if ‘auto’, it will try to invoke, for each estimator,
<code class="docutils literal notranslate"><span class="pre">'predict_proba'</span></code>, <code class="docutils literal notranslate"><span class="pre">'decision_function'</span></code> or <code class="docutils literal notranslate"><span class="pre">'predict'</span></code> in that
order.</p></li>
<li><p>otherwise, one of <code class="docutils literal notranslate"><span class="pre">'predict_proba'</span></code>, <code class="docutils literal notranslate"><span class="pre">'decision_function'</span></code> or
<code class="docutils literal notranslate"><span class="pre">'predict'</span></code>. If the method is not implemented by the estimator, it
will raise an error.</p></li>
</ul>
</dd>
<dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of jobs to run in parallel all <code class="docutils literal notranslate"><span class="pre">estimators</span></code> <code class="docutils literal notranslate"><span class="pre">fit</span></code>.
<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <code class="docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code> context. -1 means
using all processors. See Glossary for more details.</p>
</dd>
<dt><strong>passthrough</strong><span class="classifier">bool, default=False</span></dt><dd><p>When False, only the predictions of estimators will be used as
training data for <code class="docutils literal notranslate"><span class="pre">final_estimator</span></code>. When True, the
<code class="docutils literal notranslate"><span class="pre">final_estimator</span></code> is trained on the predictions as well as the
original training data.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>estimators_</strong><span class="classifier">list of estimators</span></dt><dd><p>The elements of the estimators parameter, having been fitted on the
training data. If an estimator has been set to <code class="docutils literal notranslate"><span class="pre">'drop'</span></code>, it
will not appear in <code class="docutils literal notranslate"><span class="pre">estimators_</span></code>.</p>
</dd>
<dt><strong>named_estimators_</strong><span class="classifier">Bunch</span></dt><dd><p>Attribute to access any fitted sub-estimators by name.</p>
</dd>
<dt><strong>final_estimator_</strong><span class="classifier">estimator</span></dt><dd><p>The classifier which predicts given the output of <code class="docutils literal notranslate"><span class="pre">estimators_</span></code>.</p>
</dd>
<dt><strong>stack_method_</strong><span class="classifier">list of str</span></dt><dd><p>The method used by each base estimator.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Notes</p>
<p>When <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code> is used by each estimator (i.e. most of the time for
<code class="docutils literal notranslate"><span class="pre">stack_method='auto'</span></code> or specifically for <code class="docutils literal notranslate"><span class="pre">stack_method='predict_proba'</span></code>),
The first column predicted by each estimator will be dropped in the case
of a binary classification problem. Indeed, both feature will be perfectly
collinear.</p>
<p class="rubric">References</p>
<dl class="citation">
<dt class="label" id="rb91ed47a817e-1"><span class="brackets">Rb91ed47a817e-1</span></dt>
<dd><p>Wolpert, David H. “Stacked generalization.” Neural networks 5.2
(1992): 241-259.</p>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_iris</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">LinearSVC</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <span class="n">make_pipeline</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">StackingClassifier</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">load_iris</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">estimators</span> <span class="o">=</span> <span class="p">[</span>
<span class="gp">... </span>    <span class="p">(</span><span class="s1">&#39;rf&#39;</span><span class="p">,</span> <span class="n">RandomForestClassifier</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)),</span>
<span class="gp">... </span>    <span class="p">(</span><span class="s1">&#39;svr&#39;</span><span class="p">,</span> <span class="n">make_pipeline</span><span class="p">(</span><span class="n">StandardScaler</span><span class="p">(),</span>
<span class="gp">... </span>                          <span class="n">LinearSVC</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)))</span>
<span class="gp">... </span><span class="p">]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">StackingClassifier</span><span class="p">(</span>
<span class="gp">... </span>    <span class="n">estimators</span><span class="o">=</span><span class="n">estimators</span><span class="p">,</span> <span class="n">final_estimator</span><span class="o">=</span><span class="n">LogisticRegression</span><span class="p">()</span>
<span class="gp">... </span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
<span class="gp">... </span>    <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">stratify</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span>
<span class="gp">... </span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
<span class="go">0.9...</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.ensemble.StackingClassifier.decision_function" title="sklearn.ensemble.StackingClassifier.decision_function"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decision_function</span></code></a>(self, X)</p></td>
<td><p>Predict decision function for samples in X using <code class="docutils literal notranslate"><span class="pre">final_estimator_.decision_function</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.ensemble.StackingClassifier.fit" title="sklearn.ensemble.StackingClassifier.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(self, X, y[, sample_weight])</p></td>
<td><p>Fit the estimators.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.ensemble.StackingClassifier.fit_transform" title="sklearn.ensemble.StackingClassifier.fit_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_transform</span></code></a>(self, X[, y])</p></td>
<td><p>Fit to data, then transform it.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.ensemble.StackingClassifier.get_params" title="sklearn.ensemble.StackingClassifier.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>(self[, deep])</p></td>
<td><p>Get the parameters of an estimator from the ensemble.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.ensemble.StackingClassifier.predict" title="sklearn.ensemble.StackingClassifier.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(self, X, \*\*predict_params)</p></td>
<td><p>Predict target for X.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.ensemble.StackingClassifier.predict_proba" title="sklearn.ensemble.StackingClassifier.predict_proba"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict_proba</span></code></a>(self, X)</p></td>
<td><p>Predict class probabilities for X using <code class="docutils literal notranslate"><span class="pre">final_estimator_.predict_proba</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.ensemble.StackingClassifier.score" title="sklearn.ensemble.StackingClassifier.score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">score</span></code></a>(self, X, y[, sample_weight])</p></td>
<td><p>Return the mean accuracy on the given test data and labels.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.ensemble.StackingClassifier.set_params" title="sklearn.ensemble.StackingClassifier.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(self, \*\*params)</p></td>
<td><p>Set the parameters of an estimator from the ensemble.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.ensemble.StackingClassifier.transform" title="sklearn.ensemble.StackingClassifier.transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></a>(self, X)</p></td>
<td><p>Return class labels or probabilities for X for each estimator.</p></td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.ensemble.StackingClassifier.__init__">
<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">estimators</em>, <em class="sig-param">final_estimator=None</em>, <em class="sig-param">cv=None</em>, <em class="sig-param">stack_method='auto'</em>, <em class="sig-param">n_jobs=None</em>, <em class="sig-param">passthrough=False</em>, <em class="sig-param">verbose=0</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/ensemble/_stacking.py#L368"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.__init__" title="Permalink to this definition">¶</a></dt>
<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.ensemble.StackingClassifier.decision_function">
<code class="sig-name descname">decision_function</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/utils/metaestimators.py#L459"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.decision_function" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict decision function for samples in X using
<code class="docutils literal notranslate"><span class="pre">final_estimator_.decision_function</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Training vectors, where n_samples is the number of samples and
n_features is the number of features.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>decisions</strong><span class="classifier">ndarray of shape (n_samples,), (n_samples, n_classes),             or (n_samples, n_classes * (n_classes-1) / 2)</span></dt><dd><p>The decision function computed the final estimator.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.ensemble.StackingClassifier.fit">
<code class="sig-name descname">fit</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/ensemble/_stacking.py#L389"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit the estimators.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Training vectors, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples and
<code class="docutils literal notranslate"><span class="pre">n_features</span></code> is the number of features.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>Target values.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,) or None</span></dt><dd><p>Sample weights. If None, then samples are equally weighted.
Note that this is supported only if all underlying estimators
support sample weights.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd></dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.ensemble.StackingClassifier.fit_transform">
<code class="sig-name descname">fit_transform</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y=None</em>, <em class="sig-param">**fit_params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L544"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.fit_transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit to data, then transform it.</p>
<p>Fits transformer to X and y with optional parameters fit_params
and returns a transformed version of X.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">numpy array of shape [n_samples, n_features]</span></dt><dd><p>Training set.</p>
</dd>
<dt><strong>y</strong><span class="classifier">numpy array of shape [n_samples]</span></dt><dd><p>Target values.</p>
</dd>
<dt><strong>**fit_params</strong><span class="classifier">dict</span></dt><dd><p>Additional fit parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_new</strong><span class="classifier">numpy array of shape [n_samples, n_features_new]</span></dt><dd><p>Transformed array.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.ensemble.StackingClassifier.get_params">
<code class="sig-name descname">get_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">deep=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/ensemble/_base.py#L275"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get the parameters of an estimator from the ensemble.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool</span></dt><dd><p>Setting it to True gets the various classifiers and the parameters
of the classifiers as well.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.ensemble.StackingClassifier.predict">
<code class="sig-name descname">predict</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">**predict_params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/utils/metaestimators.py#L415"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict target for X.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Training vectors, where n_samples is the number of samples and
n_features is the number of features.</p>
</dd>
<dt><strong>**predict_params</strong><span class="classifier">dict of str -&gt; obj</span></dt><dd><p>Parameters to the <code class="docutils literal notranslate"><span class="pre">predict</span></code> called by the <code class="docutils literal notranslate"><span class="pre">final_estimator</span></code>. Note
that this may be used to return uncertainties from some estimators
with <code class="docutils literal notranslate"><span class="pre">return_std</span></code> or <code class="docutils literal notranslate"><span class="pre">return_cov</span></code>. Be aware that it will only
accounts for uncertainty in the final estimator.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>y_pred</strong><span class="classifier">ndarray of shape (n_samples,) or (n_samples, n_output)</span></dt><dd><p>Predicted targets.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.ensemble.StackingClassifier.predict_proba">
<code class="sig-name descname">predict_proba</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/utils/metaestimators.py#L439"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.predict_proba" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict class probabilities for X using
<code class="docutils literal notranslate"><span class="pre">final_estimator_.predict_proba</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Training vectors, where n_samples is the number of samples and
n_features is the number of features.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>probabilities</strong><span class="classifier">ndarray of shape (n_samples, n_classes) or             list of ndarray of shape (n_output,)</span></dt><dd><p>The class probabilities of the input samples.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.ensemble.StackingClassifier.score">
<code class="sig-name descname">score</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L344"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.score" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the mean accuracy on the given test data and labels.</p>
<p>In multi-label classification, this is the subset accuracy
which is a harsh metric since you require for each sample that
each label set be correctly predicted.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>Test samples.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,) or (n_samples, n_outputs)</span></dt><dd><p>True labels for X.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>score</strong><span class="classifier">float</span></dt><dd><p>Mean accuracy of self.predict(X) wrt. y.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.ensemble.StackingClassifier.set_params">
<code class="sig-name descname">set_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">**params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/ensemble/_base.py#L257"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.set_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the parameters of an estimator from the ensemble.</p>
<p>Valid parameter keys can be listed with <code class="docutils literal notranslate"><span class="pre">get_params()</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">keyword arguments</span></dt><dd><p>Specific parameters using e.g.
<code class="docutils literal notranslate"><span class="pre">set_params(parameter_name=new_value)</span></code>. In addition, to setting the
parameters of the stacking estimator, the individual estimator of
the stacking estimators can also be set, or can be removed by
setting them to ‘drop’.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.ensemble.StackingClassifier.transform">
<code class="sig-name descname">transform</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/ensemble/_stacking.py#L479"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Return class labels or probabilities for X for each estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Training vectors, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples and
<code class="docutils literal notranslate"><span class="pre">n_features</span></code> is the number of features.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>y_preds</strong><span class="classifier">ndarray of shape (n_samples, n_estimators) or                 (n_samples, n_classes * n_estimators)</span></dt><dd><p>Prediction outputs for each estimator.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

</dd></dl>

<div class="section" id="examples-using-sklearn-ensemble-stackingclassifier">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.ensemble.StackingClassifier</span></code><a class="headerlink" href="#examples-using-sklearn-ensemble-stackingclassifier" title="Permalink to this headline">¶</a></h2>
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